On estimation of the optimal parameter of the modulus‐based matrix splitting algorithm for linear complementarity problems on second‐order cones

IF 1.8 3区 数学 Q1 MATHEMATICS
Zhizhi Li, Huai Zhang
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引用次数: 1

Abstract

There are many studies on the well‐known modulus‐based matrix splitting (MMS) algorithm for solving complementarity problems, but very few studies on its optimal parameter, which is of theoretical and practical importance. Therefore and here, by introducing a novel mapping to explicitly cast the implicit fixed point equation and thus obtain the iteration matrix involved, we first present the estimation approach of the optimal parameter of each step of the MMS algorithm for solving linear complementarity problems on the direct product of second‐order cones (SOCLCPs). It also works on single second‐order cone and the non‐negative orthant. On this basis, we further propose an iteration‐independent optimal parameter selection strategy for practical usage. Finally, the practicability and effectiveness of the new proposal are verified by comparing with the experimental optimal parameter and the diagonal part of system matrix. In addition, with the optimal parameter, the effectiveness of the MMS algorithm can indeed be greatly improved, even better than the state‐of‐the‐art solvers SCS and SuperSCS that solve the equivalent SOC programming.
二阶锥上线性互补问题的基于模的矩阵分裂算法的最优参数估计
众所周知,求解互补问题的基于模量的矩阵分裂(MMS)算法有很多研究,但对其最优参数的研究却很少,这具有重要的理论和实际意义。因此,在这里,通过引入一种新的映射来显式地投射隐式不动点方程,从而得到所涉及的迭代矩阵,我们首先给出了求解二阶锥直积线性互补问题的MMS算法每一步最优参数的估计方法。它也适用于单二阶锥和非负正交。在此基础上,我们进一步提出了一种与迭代无关的最优参数选择策略。最后,通过与实验最优参数和系统矩阵对角部分的比较,验证了新方案的实用性和有效性。此外,通过优化参数,MMS算法的有效性确实可以大大提高,甚至优于解决等效SOC规划的最先进的解算器SCS和SuperSCS。
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来源期刊
CiteScore
3.40
自引率
2.30%
发文量
50
审稿时长
12 months
期刊介绍: Manuscripts submitted to Numerical Linear Algebra with Applications should include large-scale broad-interest applications in which challenging computational results are integral to the approach investigated and analysed. Manuscripts that, in the Editor’s view, do not satisfy these conditions will not be accepted for review. Numerical Linear Algebra with Applications receives submissions in areas that address developing, analysing and applying linear algebra algorithms for solving problems arising in multilinear (tensor) algebra, in statistics, such as Markov Chains, as well as in deterministic and stochastic modelling of large-scale networks, algorithm development, performance analysis or related computational aspects. Topics covered include: Standard and Generalized Conjugate Gradients, Multigrid and Other Iterative Methods; Preconditioning Methods; Direct Solution Methods; Numerical Methods for Eigenproblems; Newton-like Methods for Nonlinear Equations; Parallel and Vectorizable Algorithms in Numerical Linear Algebra; Application of Methods of Numerical Linear Algebra in Science, Engineering and Economics.
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